{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vehicle State Plotting\n",
    "\n",
    "This example demonstrates how to\n",
    "\n",
    "1. Spawn a vehicle at a certain point and orientation on a map.\n",
    "2. Set its AI to automatically cover the drivable reachable roads on the map.\n",
    "3. Continually retrieve information about the while it's driving. This information includes:\n",
    "    1. Position\n",
    "    2. Direction\n",
    "    3. Wheel speed\n",
    "    4. Throttle intensity\n",
    "    5. Brake intensity\n",
    "\n",
    "The information obtained will then be plotted in various ways (mostly for demonstration, the plots themselves might not be particularly insightful.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "This code block will import necessary dependencies for this example. Most relevant are BeamNGpy's classes:\n",
    "\n",
    "1. `BeamNGpy`: The library's backbone that launches and communicates with the BeamNG.tech simulator.\n",
    "2. `Vehicle`: This class is used for interactions with vehicles in the simulation. It provides methods to query the state of the vehicle, control its actuators & built-in AI, and options to attach sensors that expose data in a way similar to real-life vehicle sensors.\n",
    "2. `Scenario`: A class that offers methods of scenario creation. It is used to specify which environment is to be loaded and what vehicles act in the scenario.\n",
    "\n",
    "Additionally, some sensors get included from the `sensors` package which, as the name implies, contains sensors that can be attached to a vehicle. The only one used in this demo is the `Electrics` sensor, which can be used to retrive data from the vehicle's electrical systems, such as the currently measures throttle and brake intensity or the wheel speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import time\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from beamngpy import BeamNGpy, Vehicle, Scenario\n",
    "from beamngpy.sensors import Electrics\n",
    "\n",
    "sns.set()  # Make seaborn set matplotlib styling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With dependencies imported, we can start setting up a basic scenario with one vehicle. This can easily be adapted to multiple vehicles, but for demonstration purposes we stick to one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a BeamNGpy instance the other classes use for reference & communication\n",
    "beamng = BeamNGpy('localhost', 64256)  # This is the host & port used to communicate over\n",
    "beamng.open()\n",
    "\n",
    "# Create a vehile instance that will be called 'ego' in the simulation\n",
    "# using the etk800 model the simulator ships with\n",
    "vehicle = Vehicle('ego', model='etk800', licence='PYTHON', colour='Green')\n",
    "# Create an Electrics sensor and attach it to the vehicle\n",
    "electrics = Electrics()\n",
    "vehicle.attach_sensor('electrics', electrics)\n",
    "\n",
    "# Create a scenario called vehicle_state taking place in the west_coast_usa map the simulator ships with\n",
    "scenario = Scenario('west_coast_usa', 'vehicle_state')\n",
    "# Add the vehicle and specify that it should start at a certain position and orientation.\n",
    "# The position & orientation values were obtained by opening the level in the simulator,\n",
    "# hitting F11 to open the editor and look for a spot to spawn and simply noting down the\n",
    "# corresponding values.\n",
    "scenario.add_vehicle(vehicle, pos=(-717.121, 101, 118.675), rot=(0, 0, 45))  # 45 degree rotation around the z-axis\n",
    "\n",
    "# The make function of a scneario is used to compile the scenario and produce a scenario file the simulator can load\n",
    "scenario.make(beamng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running\n",
    "\n",
    "With scenario and vehicles set up, we can launch the simulator and make it load our created scenario. This will start up the simulator, load the map specified by our scenario, and spawn the vehicles we've added to them, including their sensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "beamng.load_scenario(scenario)\n",
    "beamng.start_scenario()  # After loading, the simulator waits for further input to actually start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After loading & starting the scenario, the vehicle is located at the position specified in the scenario and awaits further input. This could be done by the user in the simulator itself through keyboard or a controller, or by calling the `control()` function of the vehicle with steering, brake, and throttle values. Besides those, the `Vehicle` class also exposes AI functions the simulator ships with. One of these is telling the AI to \"span\" the map, making it drive on every road reachable from its current position. The following code block makes the vehicle do that: drive on every road it can find from its current position.\n",
    "\n",
    "In order to actually collect data about the state of the vehicle, we also initialise some lists to store our collected data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle.ai_set_mode('span')\n",
    "\n",
    "positions = list()\n",
    "directions = list()\n",
    "wheel_speeds = list()\n",
    "throttles = list()\n",
    "brakes = list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the vehicle is driving, we enter a loop where we query the vehicle's state and poll its sensors once every 500ms. This is done 240 times, meaning the vehicle's data is captured for 120 seconds.\n",
    "\n",
    "Before the actual loop, the data we track is retrieved and printed out once for demonstration purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The vehicle position is:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[-925.3900146484375, 120.99121856689453, 148.43402099609375]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The vehicle direction is:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[-0.6738008260726929, 0.7088969349861145, 0.20846492052078247]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The wheel speed is:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "25.181835308074955"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The throttle intensity is:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9238789661619734"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The brake intensity is:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vehicle.update_vehicle()\n",
    "sensors = beamng.poll_sensors(vehicle)\n",
    "\n",
    "print('The vehicle position is:')\n",
    "display(vehicle.state['pos'])\n",
    "\n",
    "print('The vehicle direction is:')\n",
    "display(vehicle.state['dir'])\n",
    "\n",
    "print('The wheel speed is:')\n",
    "display(sensors['electrics']['wheelspeed'])\n",
    "\n",
    "print('The throttle intensity is:')\n",
    "display(sensors['electrics']['throttle'])\n",
    "\n",
    "print('The brake intensity is:')\n",
    "display(sensors['electrics']['brake'])\n",
    "\n",
    "for _ in range(240):\n",
    "    time.sleep(0.1)\n",
    "    vehicle.update_vehicle()  # Synchs the vehicle's \"state\" variable with the simulator\n",
    "    sensors = beamng.poll_sensors(vehicle)  # Polls the data of all sensors attached to the vehicle\n",
    "    positions.append(vehicle.state['pos'])\n",
    "    directions.append(vehicle.state['dir'])\n",
    "    wheel_speeds.append(sensors['electrics']['wheelspeed'])\n",
    "    throttles.append(sensors['electrics']['throttle'])\n",
    "    brakes.append(sensors['electrics']['brake'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the data we want is now captured, we can close the simulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "beamng.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "We can now display the data we've gathered. The following code serves mainly as an example to illustrate how this can be achieved. The plots themselves are not particularly useful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Throttle & brake over time.\n",
    "\n",
    "The throttle and brake intensities were collected in the `throttles` and `brakes` lists respectively. These can now be used to show respective intensities over time as data was collected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(throttles, 'b-', label='Throttle')\n",
    "plt.plot(brakes, 'r-', label='Brake')\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trace vehicle position\n",
    "\n",
    "Where the vehicle was located over time is tracked in the `positions` list. This contains a list of (x, y, z) coordinate triplets that the vehicle was observed at. These values represent the vehicle's position on the map in the simulator. To plot them, we simply take the x and y values and plot a dot for every position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [p[0] for p in positions]\n",
    "y = [p[1] for p in positions]\n",
    "plt.plot(x, y, '.')\n",
    "plt.axis('square')\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Speed and direction\n",
    "\n",
    "Combining the wheel speed values with recorded direction vectors can be used to plot in which directions the vehicle was going at what speeds. To this end, we take  the wheel speeds from the `wheelspeeds` list and calculate the angles of the corresponding direction vectors. The resulting data is then displayed as a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "angles = [np.arctan2(d[1], d[0]) for d in directions]\n",
    "r = wheel_speeds  # We simply use the speed as the radius in the radial plot\n",
    "plt.subplot(111, projection='polar')\n",
    "plt.scatter(angles, r)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
